Sentence examples for models usually include from inspiring English sources

All LMS channel models usually include two main aspects: the description of the propagation environment and the modelling of EM interactions.

Top down models usually include piecewise-smooth functions to describe marginal cost curves, while bottom up models use step function curves.

Publications of computational models usually include a prose description of the biological model in the body of the paper, with tables listing key biological components and processes.

The standard CPG models usually include the text of the CPG, however, as said previously, this text expresses recommendations but not critics, and therefore it may not be appropriate for a critiquing system.

These models usually include a broad spectrum of integration strategies and best practices such as clinical guidelines, electronic medical records, case management, capitation and performance incentives for GP remuneration, and patient self-management support [ 14, 16, 18].

Integrated care models usually included case management, and consumer-directed care usually included the assessment and individualized care plan components of case management.

Best-fit models usually included unequal base frequencies (UF) and rate variation among sites (either considering a proportion of invariable sites (+I), gamma distributed rates (+G) or both (+I+G)).

A model usually includes several hundreds different features and model training consists in learning which combinations of features are most likely to identify a brain region.

Random slope models will usually include random intercepts, too, so that we prefer the shorthand "random slope model" over the more tedious, though more precise, "random intercept, random slope model".

The mechanistic models, however, usually include some physical phenomena with analytical relations to predict the flow patterns.

The description of that strategy picked out by the model will usually include descriptions of potential behavioral responses to conditions (or values for parameters of those responses) that go beyond what has already been observed; this means the model predicts that these responses should also occur if the description of the strategy is correct.